Video object tracking is a well known method used in several computer vision-guided applications, such as security, monitoring, sports, traffic, healthcare, or the like. However, different applications have different requirements. For example, in traffic monitoring applications, tracking vehicles moving on highway may require analyzing fast moving objects of rectangular shape. Whereas in sports, tracking players and playing objects like football, tennis ball, basket ball, or the like may be desired. In surveillance applications, often objects are of unknown shape and restrictions on the movement of objects may not be applied. In such cases, tracking methods need to be robust with respect to environmental noise.
In an existing video object tracking method, a portion of an object is marked in first frame and the portion of the object that is marked is tracked in consecutive frames. A corresponding point that matches with the marked portion of the first frame within the consecutive frames is determined by minimizing the matching distance based on matching criteria. The matching criteria can be determined using information such as Sum of Absolute Differences (SAD) or sum of squared differences or any other application specific information.
In an existing block based object tracking method (single path tracking), a block representing a portion of an object is marked or detected in the first frame. A best match of the marked portion in the next frame is selected based on the minimum SAD criteria. Similarly, in each consecutive frame, the best match of the marked portion is selected and the trajectory of the block is obtained following the minimum SAD criteria. A disadvantage of the single path tracking method is that, certain errors exist while detecting the best block at each and every frame using the minimum SAD criteria. Such errors occur because of the similar color and pixel intensity of the neighboring blocks present within the frame and such errors will be cumulatively added while detecting the trajectory of the object. Even, if the best block is selected at every consecutive frame using the existing methodology, an optimal solution may not be achieved. The optimal solution may not be achieved because of the fact that the methods according to the related art depend heavily upon the success of the SAD or any other measure based inter-frame point correspondence technique. Such methods produce local maxima which may not achieve the global solution in all cases. Thus, the single path tracking method may not always achieve the optimal trajectory of the object being tracked.
In view of the above discussion, there is a need for a video object tracking method that reduces the error while detecting the minimum SAD in the single path tracking (e.g., best match criteria) to obtain the optimal trajectory of the object in a video stream.
The above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, and no assertion is made, as to whether any of the above might be applicable as prior art with regard to the present disclosure.